Prasad Bhave

Jul 15, 2021

2 min read

Being Tech-Savvy : a requirement for Healthcare Data Scientist

Data Analytics is in vogue and it is now mandatory to have basic understanding of data handling, interpretation, visualization and management, almost in every vertical or domain one is working in. The fundamental reason behind this could be traced back to hardware revolution that resulted in high speed computing and cheaper memory. This resulted in “Data” being core of every business and importance of turning Data into Wisdom ( through the steps of Data →Information →Knowledge →Insights →Wisdom )
Recognizing patterns quickly in data is the fundamental principle of every research ( more so in Medical Research ). High end computing , powerful software, large (and big) data sets, cheaper memory packed in small space , are primary reasons why Data Analytics has acquired a prime position in all the domains and verticals. Ability to handle data using IT tools is now a primary skill required in almost all domains and verticals. This is something similar to what “MS Office suite” was a required skill two decades back !

Being tech-savvy is now a critical skill required to excel as a data scientist in Healthcare, Pharmaceutical and Biotech research, Insurance and Life Sciences sector. Clinical Informatics, Healthcare Informatics and Bio-informatics has acquired prime focus as “Data” is the now “Gold” .. the new “Oil” especially in the healthcare ,medical research, pharmaceutical drug development sector.

For upcoming Clinical informaticians / Healthcare Data Scientist , here is my recommendation on basic skills required to be a part of a Data Science, Data Analytics group, especially in HLS domain :
MS Office (Excel,PPT,Word) with expertise in handling Excel sheets
Data Entry, analysis, Interpretation :SPSS, SAS , R , Python
Data Handling : RDBMS , SQL
Data Visualization: Matlab, Tableau
AI-ML related modeling and automation :TensorFlow , SAP-HANA

I am sure that newer and more user friendly interfaces will make data handling easier and automated over next couple of years. This will help the domain experts concentrate more on data interpretation and analysis. Till then we need to be good at the above skill sets to deliver data driven decisions.

P.S : Recommendation for Data Analytics professionals in Healthcare, Pharmaceutical domains: Excel sheet and tools (still my favorite sometimes!), SPSS ,SAS , R , Python, Matlab, Tableau , TensorFlow, SAP-HANA .. and more !

Related further readings :

Key Concepts in Bio-statistics for Data Scientists

A day in the life of Data

A graphical overview of Data Analytics for Value Based Healthcare

Population Health Management : Business side of Healthcare

Quality Management principles in Healthcare

Pillars of Value Based Healthcare : RCM

Population Health Management : Data Analytics